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Analysis and Classification of Evoked Potentials in Response to Familiar and Unfamiliar Faces

dc.contributor.editorCallejas J.D.C.
dc.creatorSanchez-Hernandez S.A.
dc.creatorContreras-Ortiz S.H.
dc.date.accessioned2020-03-26T16:33:13Z
dc.date.available2020-03-26T16:33:13Z
dc.date.issued2018
dc.identifier.citation2018 IEEE ANDESCON, ANDESCON 2018 - Conference Proceedings
dc.identifier.isbn9781538683729
dc.identifier.urihttps://hdl.handle.net/20.500.12585/9204
dc.description.abstractBrain activity during perception and recognition of faces have been studied by researchers with the purpose to develop brain-computer interfaces and to study neurological disorders. In this paper, we analyzed evoked potentials as neurophysiological indicators and developed a model based on signal processing and machine learning techniques to find descriptive patterns that allow the differentiation of familiar and unfamiliar faces. We considered wave components such as P1, N170, N250, P300, and N400 to describe the events. Morphological analysis and wavelet transform were used for the feature extraction stage, and support vector machines and binomial logistic regression were evaluated for the classification stage. The best classification results were obtained with the morphological characteristics, where the highest classification accuracy was 80% on average. © 2018 IEEE.eng
dc.description.sponsorshipInstitute of Electrical and Electronics Engineers Colombia Section;Institute of Electrical and Electronics Engineers Consejo Andino
dc.format.mediumRecurso electrónico
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.sourcehttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85060377163&doi=10.1109%2fANDESCON.2018.8564591&partnerID=40&md5=636208c54b94118642f0da270a42a1ab
dc.sourceScopus2-s2.0-85060377163
dc.titleAnalysis and Classification of Evoked Potentials in Response to Familiar and Unfamiliar Faces
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datacite.rightshttp://purl.org/coar/access_right/c_16ec
oaire.resourceTypehttp://purl.org/coar/resource_type/c_c94f
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
dc.source.event9th IEEE ANDESCON, ANDESCON 2018
dc.type.driverinfo:eu-repo/semantics/conferenceObject
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersion
dc.identifier.doi10.1109/ANDESCON.2018.8564591
dc.subject.keywordsElectroencephalography
dc.subject.keywordsEvoked potentials
dc.subject.keywordsFace recognition
dc.subject.keywordsMachine learning
dc.subject.keywordsWavelet transform
dc.subject.keywordsArtificial intelligence
dc.subject.keywordsBioelectric potentials
dc.subject.keywordsBiomedical signal processing
dc.subject.keywordsBrain
dc.subject.keywordsBrain computer interface
dc.subject.keywordsElectroencephalography
dc.subject.keywordsElectrophysiology
dc.subject.keywordsLearning systems
dc.subject.keywordsNeurophysiology
dc.subject.keywordsWavelet transforms
dc.subject.keywordsBinomial logistic regressions
dc.subject.keywordsClassification accuracy
dc.subject.keywordsClassification results
dc.subject.keywordsFeature extraction stages
dc.subject.keywordsMachine learning techniques
dc.subject.keywordsMorphological analysis
dc.subject.keywordsMorphological characteristic
dc.subject.keywordsPerception and recognition
dc.subject.keywordsFace recognition
dc.rights.accessRightsinfo:eu-repo/semantics/restrictedAccess
dc.rights.ccAtribución-NoComercial 4.0 Internacional
dc.identifier.instnameUniversidad Tecnológica de Bolívar
dc.identifier.reponameRepositorio UTB
dc.relation.conferencedate22 August 2018 through 24 August 2018
dc.type.spaConferencia
dc.identifier.orcid57205528869
dc.identifier.orcid57210822856


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